Fully automated segmentation-based respiratory motion correction of multiplanar cardiac magnetic resonance images for large-scale datasets

Matthew Sinclair*, Wenjia Bai, Esther Puyol-Antón, Ozan Oktay, Daniel Rueckert, Andrew P. King

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingOther chapter contributionpeer-review

14 Citations (Scopus)

Abstract

Cardiac magnetic resonance (CMR) can be used for quantitative analysis of heart function. However, CMR imaging typically involves acquiring 2D image planes during separate breath-holds, often resulting in misalignment of the heart between image planes in 3D. Accurate quantitative analysis requires a robust 3D reconstruction of the heart from CMR images, which is adversely affected by such motion artifacts. Therefore, we propose a fully automated method for motion correction of CMR planes using segmentations produced by fully convolutional neural networks (FCNs). FCNs are trained on 100 UK Biobank subjects to produce short-axis and long-axis segmentations, which are subsequently used in an iterative registration algorithm for correcting breath-hold induced motion artifacts. We demonstrate significant improvements in motion-correction over image-based registration, with strong correspondence to results obtained using manual segmentations. We also deploy our automatic method on 9,353 subjects in the UK Biobank database, demonstrating significant improvements in 3D plane alignment.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
PublisherSpringer Verlag
Pages332-340
Number of pages9
Volume10434 LNCS
ISBN (Print)9783319661841
DOIs
Publication statusPublished - 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 11 Sept 201713 Sept 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10434 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Country/TerritoryCanada
CityQuebec City
Period11/09/201713/09/2017

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